markdown .md

stripped down html

• intended to be as easy-to-read and easy-to-write as possible.
• intended for one purpose: to be used as a format for writing for the web.
• syntax is very small, corresponding only to a very small subset of HTML tags.

focus on communicating & disseminating

• formatting handled automatically
• clean and legible across platforms and outputs

rmarkdown .Rmd

literate programming

single document to integrate data analysis with textual representations, linking data, code, and text are not linked

– A documentantion language – A programming language

rmarkdown integrates R with md

outputs

• html
• presentations
• pdf
• word documents

it’s everywhere!

• github READMEs; eg rOpensci taxise README

• stackoverflow: eg plot coordinates on a map

• github.io websites: eg Andy South’s blog

computation & science

Computational science has led to exciting new developments:

• Technology is increasing data collection throughput; data are more complex and highdimensional
• Existing databases can be merged to become bigger databases
• Computing power allows more sophisticated analyses, even on “small” data
• For every field “X” there is a “Computational X”

Increasing computational complexity of analyses:

the nature of the work has exposed limitations in our ability to evaluate published findings.

- Even basic analyses difficult to describe

• Heavy computational requirements thrust upon people without adequate training in statistics and computing

• Errors more easily introduced into long analysis pipelines

• Knowledge transfer is inhibited

• Results are difficult to replicate or reproduce

• Complicated analyses cannot be trusted

calls for reproducibility

Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.

• fully scripted analyses
• make code and data available

reproducibility limitations

• top down
• downstream

evidence based

evdence needs:

• documenting
• linking
• communicating

rmarkdown is the glue that knits the tools, processes and output into evidence trails

at all stages of science

simple tools: low hanging fruit

• begin at the start of the process
• document & interlink evidence stream
• explore and communicate!

empower your code and data

examples

report

code documentation

method collation

interactive documents

presentations

text

    normal text

normal text

    *italic text*

italic text

    **bold text**

bold text

    **bold italic text**

bold italic text

    superscript^2^

superscript²

    ~~strikethrough~~

strikethrough

headers

unordered lists

ordered lists

quotes & code

> this text will be quoted

this text will be quoted

`this text will appear as code` inline

this text will appear as code inline

a <- 10

    the value of parameter *a* is `r a`

the value of parameter a is 10

text formatting

images

    ![](https://www.rstudio.com/wp-content/uploads/2015/01/rmarkdown-cheatsheet-2-e1457627578814.png)
    
    ![](resources/cheat.png)
    

resize images

    <img src="resources/cheat.png" width="200px" />

basic tables

Table Header  | Second Header
------------- | -------------
Cell 1        | Cell 2
Cell 3        | Cell 4 

online table to .md converter

links

[Johns Hopkins Bloomberg School of Public Health](http://www.jhsph.edu/)
[Download R](http://www.r-project.org/)    
[RStudio](http://www.rstudio.com/)

Johns Hopkins Bloomberg School of Public Health

Download R

RStudio

.md resources

offical markdown documentation

gtihub .md basics

stackoverflow .md basics

github.io websites: eg Andy South’s blog

R code chunks can be used as a means render R output into documents or to simply display code for illustration

options

for more details see http://yihui.name/knitr/

knitr::kable() tables

require(knitr)
data(airquality)
kable(airquality, caption = "New York Air Quality Measurements")

DT::kable() tables

require(DT)
data(airquality)
datatable(airquality, caption = "New York Air Quality Measurements")

plotly

library(plotly)

set.seed(100)
d <- diamonds[sample(nrow(diamonds), 1000), ]

p <- ggplot(data = d, aes(x = carat, y = price)) +
  geom_point(aes(text = paste("Clarity:", clarity)), size = 1) +
  geom_smooth(aes(colour = cut, fill = cut)) + facet_wrap(~ cut)

ggplotly(p)

shiny

outputs

demo

your mission

create your first .Rmd!

• get data from OSF
• show us some data in a table
• plot some data
• fit a model
• write a bit about what you did

see my example: